Numerical Investigation of Fixed-Bed Downdraft Woody Biomass Gasification

Aydin, Ebubekir Sıddık; Yücel, Özgün; Sadıkoğlu, Hasan

doi:10.1016/b978-0-12-813734-5.00018-4

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…The method is often used to calculate the number of error values (Bostan et al, 2012). The smaller the RMSE value, the better the accuracy of the calculation model (Aydin et al, 2018). The RMSE formula is as follows:…”

Section: Accuracy Testmentioning

confidence: 99%

Comparison of Coal Reserve Estimation Methods, Case Study PT. Bukit Asam Area, South Sumatra, Indonesia

et al. 2021

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The calculation of coal reserves is influenced by the dimensions or size of the coal deposit. There are several types of coal reserve calculation methods, and the use of these methods is adjusted to existing geological conditions. Each method will produce a different amount of coal reserves, although the location is the same. Besides, the amount of coal mining that can be produced is primarily determined by the mine design, especially the optimal slope as a basis for mining pits in the coal extraction. This research aims to estimate coal reserves based on existing pit designs using a variety of methods. Data on coal thickness and topography are used as the basis for reserves estimation. Coal reserve estimation is conducted in several methods: nearest neighbor point (NNP), inverse distance weighted (IDW), and kriging using Surfer 13 software. The results of the reserves estimation indicate that kriging is the best method by providing the smallest error value with an RMSE value of 0.67 and coal reserves of 27,801,543 tons.

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Section: Accuracy Testmentioning

confidence: 99%

Comparison of Coal Reserve Estimation Methods, Case Study PT. Bukit Asam Area, South Sumatra, Indonesia

et al. 2021

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“…There are many different methods to harness the bio-energy in sludge, such as, pyrolysis, gasification, dark fermentation and anaerobic digestion. While gasification converts biomass into a combustible gas mixture-mostly carbon dioxide and hydrogen-anaerobic digestion transforms the volatile suspended solids into biogas [18,19]. Shizas and Bagley [20] claim that the energy potential of a raw wastewater is 9.3 times higher than the required energy to treat it.…”

Section: Introductionmentioning

confidence: 99%

Can a Wastewater Treatment Plant Power Itself? Results from a Novel Biokinetic-Thermodynamic Analysis

Erguvan

MacPhee

2021

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The water–energy nexus (WEN) has become increasingly important due to differences in supply and demand of both commodities. At the center of the WEN is wastewater treatment plants (WWTP), which can consume a significant portion of total electricity usage in many developed countries. In this study, a novel multigeneration energy system has been developed to provide an energetically self-sufficient WWTP. This system consists of four major subsystems: an activated sludge process, an anerobic digester, a gas power (Brayton) cycle, and a steam power (Rankine) cycle. Furthermore, a novel secondary compressor has been attached to the Brayton cycle to power aeration in the activated sludge system in order to increase the efficiency of the overall system. The energy and exergy efficiencies have been investigated by varying several parameters in both WWTP and power cycles. The effect of these parameters (biological oxygen demand, dissolved oxygen level, turbine inlet temperature, compression ratio and preheater temperature) on the self-efficiency has also been investigated. It was found here that up to 109% of the wastewater treatment energy demand can be produced using the proposed system. The turbine inlet temperature of the Brayton cycle has the largest effect on self-sufficiency of the system. Energy and exergy efficiencies of the overall system varied from 35.7% to 46.0% and from 30.6% to 33.55%, respectively.

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“…Different model studies have been employed to examine the effects of the variables affecting the gasification performance to evaluate product gas yield and composition . Depending on the complexity of the gasification, these models may be 3D fluid dynamical, artificial neural networks, artificial intelligence, kinetic‐based, or less complex equilibrium models . Even though none of the mathematical model is perfect for expressing or representing the gasification system, there is still a need for developing mathematical models .…”

Section: Introductionmentioning

confidence: 99%

“…11 Depending on the complexity of the gasification, these models may be 3D fluid dynamical, artificial neural networks, artificial intelligence, kinetic-based, or less complex equilibrium models. [12][13][14][15][16][17][18][19] Even though none of the mathematical model is perfect for expressing or representing the gasification system, there is still a need for developing mathematical models. 20 For computational fluid dynamics (CFD) models, a set of energy, momentum, and mass equations are solved to find the distribution of various parameters such as concentration and temperature over a certain region of a gasifier.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the different artificial neural networks in prediction of biomass gasification products

2019

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Summary In this study, artificial neural networks (ANNs) and a nonlinear autoregressive exogenous (NARX) neural network model were employed in order to model a fixed bed downdraft gasification. The relation between the feature group and the regression performance was investigated. First, feature group consists of the equivalence ratio (ER), air flow rate (AF), and temperature distribution (T0‐T5) obtained from the fixed bed downdraft gasifiers, while the second group includes ultimate and proximate values of biomasses, ER, AF, and the reduction temperature (T0). Models constructed to predict the syngas composition (H2, CO2, CO, CH4) and calorific value. Experimental gasification data that involve 3831 data samples that belong to pinecone and wood pellet were used for training the ANNs. Different ANN architecture and NARX time series model have been constructed to examine the prediction accuracy of the models. The results of the ANN models were consistent with the experimental data (R2 > 0.99). The overall score of NARX time series networks is found to be higher than other architecture types. A successful method is proposed to reduce the number of features, and the effect of the features on the prediction capability was examined by calculating the relative importance index using the Garson's equation.

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Numerical Investigation of Fixed-Bed Downdraft Woody Biomass Gasification

Cited by 6 publications

References 21 publications

Comparison of Coal Reserve Estimation Methods, Case Study PT. Bukit Asam Area, South Sumatra, Indonesia

Comparison of Coal Reserve Estimation Methods, Case Study PT. Bukit Asam Area, South Sumatra, Indonesia

Can a Wastewater Treatment Plant Power Itself? Results from a Novel Biokinetic-Thermodynamic Analysis

Comparison of the different artificial neural networks in prediction of biomass gasification products

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